def test_get_s3_file_upload_extra_args_invalid_json():
os.environ.setdefault(
"MLFLOW_S3_UPLOAD_EXTRA_ARGS",
'"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": "123456"}')
with pytest.raises(ValueError):
S3ArtifactRepository.get_s3_file_upload_extra_args()
def test_model_load_from_remote_uri_succeeds(sklearn_knn_model,
main_scoped_model_class, tmpdir,
mock_s3_bucket, iris_data):
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_repo = S3ArtifactRepository(artifact_root)
sklearn_model_path = os.path.join(str(tmpdir), "sklearn_model")
mlflow.sklearn.save_model(sk_model=sklearn_knn_model,
path=sklearn_model_path)
sklearn_artifact_path = "sk_model"
artifact_repo.log_artifacts(sklearn_model_path,
artifact_path=sklearn_artifact_path)
def test_predict(sk_model, model_input):
return sk_model.predict(model_input) * 2
pyfunc_model_path = os.path.join(str(tmpdir), "pyfunc_model")
mlflow.pyfunc.save_model(
path=pyfunc_model_path,
artifacts={"sk_model": sklearn_model_path},
python_model=main_scoped_model_class(test_predict),
conda_env=_conda_env())
pyfunc_artifact_path = "pyfunc_model"
artifact_repo.log_artifacts(pyfunc_model_path,
artifact_path=pyfunc_artifact_path)
model_uri = artifact_root + "/" + pyfunc_artifact_path
loaded_pyfunc_model = mlflow.pyfunc.load_pyfunc(model_uri=model_uri)
np.testing.assert_array_equal(
loaded_pyfunc_model.predict(model_input=iris_data[0]),
test_predict(sk_model=sklearn_knn_model, model_input=iris_data[0]))
def test_load_model_from_remote_uri_succeeds(saved_tf_iris_model, model_path,
mock_s3_bucket):
mlflow.tensorflow.save_model(
tf_saved_model_dir=saved_tf_iris_model.path,
tf_meta_graph_tags=saved_tf_iris_model.meta_graph_tags,
tf_signature_def_key=saved_tf_iris_model.signature_def_key,
path=model_path,
)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
infer = mlflow.tensorflow.load_model(model_uri=model_uri)
feed_dict = {
df_column_name:
tf.constant(saved_tf_iris_model.inference_df[df_column_name])
for df_column_name in list(saved_tf_iris_model.inference_df)
}
raw_preds = infer(**feed_dict)
pred_dict = {
column_name: raw_preds[column_name].numpy()
for column_name in raw_preds.keys()
}
for col in pred_dict:
assert np.allclose(
np.array(pred_dict[col], dtype=np.float),
np.array(saved_tf_iris_model.raw_results[col], dtype=np.float),
)
def _upload_s3(local_model_path, bucket, prefix, region_name, s3_client):
"""
Upload dir to S3 as .tar.gz.
:param local_model_path: Local path to a dir.
:param bucket: S3 bucket where to store the data.
:param prefix: Path within the bucket.
:param region_name: The AWS region in which to upload data to S3.
:param s3_client: A boto3 client for S3.
:return: S3 path of the uploaded artifact.
"""
import boto3
sess = boto3.Session(region_name=region_name)
with TempDir() as tmp:
model_data_file = tmp.path("model.tar.gz")
_make_tarfile(model_data_file, local_model_path)
with open(model_data_file, "rb") as fobj:
key = os.path.join(prefix, "model.tar.gz")
obj = sess.resource("s3").Bucket(bucket).Object(key)
environ_extra_args = S3ArtifactRepository.get_s3_file_upload_extra_args(
)
obj.upload_fileobj(fobj, ExtraArgs=environ_extra_args)
response = s3_client.put_object_tagging(
Bucket=bucket,
Key=key,
Tagging={"TagSet": [{
"Key": "SageMaker",
"Value": "true"
}]})
_logger.info("tag response: %s", response)
return "{}/{}/{}".format(s3_client.meta.endpoint_url, bucket, key)
def test_load_model_from_remote_uri_succeeds(saved_tf_iris_model, model_path,
mock_s3_bucket):
mlflow.tensorflow.save_model(
tf_saved_model_dir=saved_tf_iris_model.path,
tf_meta_graph_tags=saved_tf_iris_model.meta_graph_tags,
tf_signature_def_key=saved_tf_iris_model.signature_def_key,
path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
tf_graph = tf.Graph()
tf_sess = tf.Session(graph=tf_graph)
with tf_graph.as_default():
signature_def = mlflow.tensorflow.load_model(model_uri=model_uri,
tf_sess=tf_sess)
for _, input_signature in signature_def.inputs.items():
t_input = tf_graph.get_tensor_by_name(input_signature.name)
assert t_input is not None
for _, output_signature in signature_def.outputs.items():
t_output = tf_graph.get_tensor_by_name(output_signature.name)
assert t_output is not None
def test_cli_build_image_with_remote_uri_calls_expected_azure_routines(
sklearn_model, model_path, mock_s3_bucket):
mlflow.sklearn.save_model(sk_model=sklearn_model, path=model_path)
artifact_path = "model"
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
s3_artifact_repo = S3ArtifactRepository(artifact_root)
s3_artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
with AzureMLMocks() as aml_mocks:
result = CliRunner(env={
"LC_ALL": "en_US.UTF-8",
"LANG": "en_US.UTF-8"
}).invoke(
mlflow.azureml.cli.commands,
[
"build-image",
"-m",
model_uri,
"-w",
"test_workspace",
"-i",
"image_name",
"-n",
"model_name",
],
)
assert result.exit_code == 0
assert aml_mocks["register_model"].call_count == 1
assert aml_mocks["create_image"].call_count == 1
assert aml_mocks["load_workspace"].call_count == 1
def test_get_s3_file_upload_extra_args():
os.environ.setdefault(
"MLFLOW_S3_UPLOAD_EXTRA_ARGS",
'{"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": "123456"}',
)
parsed_args = S3ArtifactRepository.get_s3_file_upload_extra_args()
assert parsed_args == {"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": "123456"}
def test_deploy_cli_creates_sagemaker_transform_job_and_s3_resources_with_expected_names_from_s3(
pretrained_model, sagemaker_client):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path,
artifact_path=artifact_path)
model_s3_uri = "s3://{bucket_name}/{artifact_path}".format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path)
job_name = "test-job"
result = CliRunner(env={
"LC_ALL": "en_US.UTF-8",
"LANG": "en_US.UTF-8"
}).invoke(
mfscli.commands,
[
"deploy-transform-job",
"--job-name",
job_name,
"--model-uri",
model_s3_uri,
"--input-data-type",
"Some Data Type",
"--input-uri",
"Some Input Uri",
"--content-type",
"Some Content Type",
"--output-path",
"Some Output Path",
"--archive",
],
)
assert result.exit_code == 0
region_name = sagemaker_client.meta.region_name
s3_client = boto3.client("s3", region_name=region_name)
default_bucket = mfs._get_default_s3_bucket(region_name)
transform_job_description = sagemaker_client.describe_transform_job(
TransformJobName=job_name)
model_name = transform_job_description["ModelName"]
assert model_name in [
model["ModelName"]
for model in sagemaker_client.list_models()["Models"]
]
object_names = [
entry["Key"]
for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert job_name in [
transform_job["TransformJobName"] for transform_job in
sagemaker_client.list_transform_jobs()["TransformJobSummaries"]
]
def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3(
pretrained_model, sagemaker_client):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path,
artifact_path=artifact_path)
model_s3_uri = "s3://{bucket_name}/{artifact_path}".format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path)
app_name = "test-app"
result = CliRunner(env={
"LC_ALL": "en_US.UTF-8",
"LANG": "en_US.UTF-8"
}).invoke(
mfscli.commands,
[
"deploy", "-a", app_name, "-m", model_s3_uri, "--mode",
mfs.DEPLOYMENT_MODE_CREATE
],
)
assert result.exit_code == 0
region_name = sagemaker_client.meta.region_name
s3_client = boto3.client("s3", region_name=region_name)
default_bucket = mfs._get_default_s3_bucket(region_name)
endpoint_description = sagemaker_client.describe_endpoint(
EndpointName=app_name)
endpoint_production_variants = endpoint_description["ProductionVariants"]
assert len(endpoint_production_variants) == 1
model_name = endpoint_production_variants[0]["VariantName"]
assert model_name in [
model["ModelName"]
for model in sagemaker_client.list_models()["Models"]
]
object_names = [
entry["Key"]
for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert any([
app_name in config["EndpointConfigName"] for config in
sagemaker_client.list_endpoint_configs()["EndpointConfigs"]
])
assert app_name in [
endpoint["EndpointName"]
for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
]
model_environment = sagemaker_client.describe_model(
ModelName=model_name)["PrimaryContainer"]["Environment"]
assert model_environment == {
"MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
"SERVING_ENVIRONMENT": "SageMaker",
}
def test_model_load_from_remote_uri_succeeds(model, model_path, mock_s3_bucket, data, predicted):
x, _ = data
mlflow.keras.save_model(model, model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
model_loaded = mlflow.keras.load_model(model_uri=model_uri)
assert all(model_loaded.predict(x) == predicted)
def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_from_s3(
pretrained_model, sagemaker_client):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository('s3://{}'.format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path,
artifact_path=artifact_path)
model_s3_uri = 's3://{bucket_name}/{artifact_path}'.format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path)
app_name = "test-app"
result = CliRunner(env={
"LC_ALL": "en_US.UTF-8",
"LANG": "en_US.UTF-8"
}).invoke(mfscli.commands, [
'deploy',
'-a',
app_name,
'-m',
model_s3_uri,
'--mode',
mfs.DEPLOYMENT_MODE_CREATE,
])
assert result.exit_code == 0
region_name = sagemaker_client.meta.region_name
s3_client = boto3.client("s3", region_name=region_name)
default_bucket = mfs._get_default_s3_bucket(region_name)
endpoint_description = sagemaker_client.describe_endpoint(
EndpointName=app_name)
endpoint_production_variants = endpoint_description["ProductionVariants"]
assert len(endpoint_production_variants) == 1
model_name = endpoint_production_variants[0]["VariantName"]
assert model_name in [
model["ModelName"]
for model in sagemaker_client.list_models()["Models"]
]
object_names = [
entry["Key"]
for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert any([
app_name in config["EndpointConfigName"] for config in
sagemaker_client.list_endpoint_configs()["EndpointConfigs"]
])
assert app_name in [
endpoint["EndpointName"]
for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
]
def test_load_model_from_remote_uri_succeeds(
sequential_model, model_path, mock_s3_bucket, data, sequential_predicted
):
mlflow.pytorch.save_model(sequential_model, model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
sequential_model_loaded = mlflow.pytorch.load_model(model_uri=model_uri)
np.testing.assert_array_equal(_predict(sequential_model_loaded, data), sequential_predicted)
def test_model_load_from_remote_uri_succeeds(reg_model, model_path, mock_s3_bucket):
model, inference_dataframe = reg_model
mlflow.catboost.save_model(cb_model=model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_path = "model"
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
loaded_model = mlflow.catboost.load_model(model_uri=model_uri)
np.testing.assert_array_almost_equal(
model.predict(inference_dataframe), loaded_model.predict(inference_dataframe),
)
def test_diviner_load_from_remote_uri_succeeds(grouped_pmdarima, model_path, mock_s3_bucket):
mlflow.diviner.save_model(diviner_model=grouped_pmdarima, path=model_path)
artifact_root = f"s3://{mock_s3_bucket}"
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
# NB: cloudpathlib would need to be used here to handle object store uri
model_uri = os.path.join(artifact_root, artifact_path)
reloaded_model = mlflow.diviner.load_model(model_uri=model_uri)
pd.testing.assert_frame_equal(grouped_pmdarima.predict(10), reloaded_model.predict(10))
def test_model_load_from_remote_uri_succeeds(sklearn_knn_model, model_path, mock_s3_bucket):
mlflow.sklearn.save_model(sk_model=sklearn_knn_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_knn_model = mlflow.sklearn.load_model(model_uri=model_uri)
np.testing.assert_array_equal(
sklearn_knn_model.model.predict(sklearn_knn_model.inference_data),
reloaded_knn_model.predict(sklearn_knn_model.inference_data))
def test_sparkml_model_load_from_remote_uri_succeeds(spark_model_iris, model_path, mock_s3_bucket):
sparkm.save_model(spark_model=spark_model_iris.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = sparkm.load_model(model_uri=model_uri)
preds_df = reloaded_model.transform(spark_model_iris.spark_df)
preds = [x.prediction for x in preds_df.select("prediction").collect()]
assert spark_model_iris.predictions == preds
def test_model_load_from_remote_uri_succeeds(xgb_model, model_path, mock_s3_bucket):
mlflow.xgboost.save_model(xgb_model=xgb_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = mlflow.xgboost.load_model(model_uri=model_uri)
np.testing.assert_array_almost_equal(
xgb_model.model.predict(xgb_model.inference_dmatrix),
reloaded_model.predict(xgb_model.inference_dmatrix))
def test_model_load_from_remote_uri_succeeds(prophet_model, model_path, mock_s3_bucket):
mlflow.prophet.save_model(pr_model=prophet_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = os.path.join(artifact_root, artifact_path)
reloaded_prophet_model = mlflow.prophet.load_model(model_uri=model_uri)
np.testing.assert_array_equal(
generate_forecast(prophet_model.model, FORECAST_HORIZON),
generate_forecast(reloaded_prophet_model, FORECAST_HORIZON),
)
def test_model_load_from_remote_uri_succeeds(pd_model, model_path, mock_s3_bucket):
mlflow.paddle.save_model(pd_model=pd_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = mlflow.paddle.load_model(model_uri=model_uri)
np.testing.assert_array_almost_equal(
pd_model.model(pd_model.inference_dataframe),
reloaded_model(pd_model.inference_dataframe),
decimal=5,
)
def test_model_load_from_remote_uri_succeeds(arma_model, model_path, mock_s3_bucket):
mlflow.statsmodels.save_model(statsmodels_model=arma_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = mlflow.statsmodels.load_model(model_uri=model_uri)
start_date, end_date = _get_dates_from_df(arma_model.inference_dataframe)
np.testing.assert_array_almost_equal(
arma_model.model.predict(start=start_date, end=end_date),
reloaded_model.predict(start=start_date, end=end_date),
)
def test_deploy_with_remote_uri_calls_expected_azure_routines(
sklearn_model, model_path, mock_s3_bucket):
mlflow.sklearn.save_model(sk_model=sklearn_model, path=model_path)
artifact_path = "model"
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
s3_artifact_repo = S3ArtifactRepository(artifact_root)
s3_artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
with AzureMLMocks() as aml_mocks:
workspace = get_azure_workspace()
mlflow.azureml.deploy(model_uri=model_uri, workspace=workspace)
assert aml_mocks["register_model"].call_count == 1
assert aml_mocks["model_deploy"].call_count == 1
def test_pmdarima_load_from_remote_uri_succeeds(auto_arima_object_model,
model_path, mock_s3_bucket):
mlflow.pmdarima.save_model(pmdarima_model=auto_arima_object_model,
path=model_path)
artifact_root = f"s3://{mock_s3_bucket}"
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = os.path.join(artifact_root, artifact_path)
reloaded_pmdarima_model = mlflow.pmdarima.load_model(model_uri=model_uri)
np.testing.assert_array_equal(auto_arima_object_model.predict(30),
reloaded_pmdarima_model.predict(30))
def test_create_deployment_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3(
pretrained_model, sagemaker_client, sagemaker_deployment_client
):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path, artifact_path=artifact_path)
model_s3_uri = "s3://{bucket_name}/{artifact_path}".format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path
)
name = "test-app"
sagemaker_deployment_client.create_deployment(
name=name,
model_uri=model_s3_uri,
)
endpoint_description = sagemaker_client.describe_endpoint(EndpointName=name)
endpoint_production_variants = endpoint_description["ProductionVariants"]
assert len(endpoint_production_variants) == 1
model_name = endpoint_production_variants[0]["VariantName"]
assert model_name in [model["ModelName"] for model in sagemaker_client.list_models()["Models"]]
s3_client = boto3.client("s3", region_name=region_name)
object_names = [
entry["Key"] for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert any(
[
name in config["EndpointConfigName"]
for config in sagemaker_client.list_endpoint_configs()["EndpointConfigs"]
]
)
assert name in [
endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
]
model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][
"Environment"
]
assert model_environment == {
"MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
"SERVING_ENVIRONMENT": "SageMaker",
}
def test_model_load_from_remote_uri_succeeds(fastai_model, model_path, mock_s3_bucket):
model = fastai_model.model
mlflow.fastai.save_model(fastai_learner=fastai_model.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = mlflow.fastai.load_model(model_uri=model_uri)
model_wrapper = mlflow.fastai._FastaiModelWrapper(model)
reloaded_model_wrapper = mlflow.fastai._FastaiModelWrapper(reloaded_model)
compare_wrapper_results(
model_wrapper.predict(fastai_model.inference_dataframe),
reloaded_model_wrapper.predict(fastai_model.inference_dataframe))
def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_from_s3(
pretrained_model, sagemaker_client):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path,
artifact_path=artifact_path)
model_s3_uri = "s3://{bucket_name}/{artifact_path}".format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path)
app_name = "test-app"
mfs.deploy(app_name=app_name,
model_uri=model_s3_uri,
mode=mfs.DEPLOYMENT_MODE_CREATE)
endpoint_description = sagemaker_client.describe_endpoint(
EndpointName=app_name)
endpoint_production_variants = endpoint_description["ProductionVariants"]
assert len(endpoint_production_variants) == 1
model_name = endpoint_production_variants[0]["VariantName"]
assert model_name in [
model["ModelName"]
for model in sagemaker_client.list_models()["Models"]
]
s3_client = boto3.client("s3", region_name=region_name)
object_names = [
entry["Key"]
for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert any([
app_name in config["EndpointConfigName"] for config in
sagemaker_client.list_endpoint_configs()["EndpointConfigs"]
])
assert app_name in [
endpoint["EndpointName"]
for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
]
def test_deploy_creates_sagemaker_transform_job_and_s3_resources_with_expected_names_from_s3(
pretrained_model, sagemaker_client):
local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
artifact_path = "model"
region_name = sagemaker_client.meta.region_name
default_bucket = mfs._get_default_s3_bucket(region_name)
s3_artifact_repo = S3ArtifactRepository("s3://{}".format(default_bucket))
s3_artifact_repo.log_artifacts(local_model_path,
artifact_path=artifact_path)
model_s3_uri = "s3://{bucket_name}/{artifact_path}".format(
bucket_name=default_bucket, artifact_path=pretrained_model.model_path)
job_name = "test-job"
mfs.deploy_transform_job(
job_name=job_name,
model_uri=model_s3_uri,
s3_input_data_type="Some Data Type",
s3_input_uri="Some Input Uri",
content_type="Some Content Type",
s3_output_path="Some Output Path",
archive=True,
)
transform_job_description = sagemaker_client.describe_transform_job(
TransformJobName=job_name)
model_name = transform_job_description["ModelName"]
assert model_name in [
model["ModelName"]
for model in sagemaker_client.list_models()["Models"]
]
s3_client = boto3.client("s3", region_name=region_name)
object_names = [
entry["Key"]
for entry in s3_client.list_objects(Bucket=default_bucket)["Contents"]
]
assert any([model_name in object_name for object_name in object_names])
assert job_name in [
transform_job["TransformJobName"] for transform_job in
sagemaker_client.list_transform_jobs()["TransformJobSummaries"]
]
def test_model_built_in_high_level_api_load_from_remote_uri_succeeds(
pd_model_built_in_high_level_api, model_path, mock_s3_bucket):
model = pd_model_built_in_high_level_api.model
test_dataset = pd_model_built_in_high_level_api.inference_dataframe
mlflow.paddle.save_model(pd_model=model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = mlflow.paddle.load_model(model_uri=model_uri)
low_level_test_dataset = [x[0] for x in test_dataset]
np.testing.assert_array_almost_equal(
np.array(model.predict(test_dataset)).squeeze(),
np.array(reloaded_model(np.array(low_level_test_dataset))).squeeze(),
decimal=5,
)
def test_get_s3_file_upload_extra_args_env_var_not_present():
parsed_args = S3ArtifactRepository.get_s3_file_upload_extra_args()
assert parsed_args is None
